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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03710naa a2200433 4500 | |
| 001 | oai:DiVA.org:su-84774 | |
| 003 | SwePub | |
| 008 | 130102s2012 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-847742 URI |
| 024 | 7 | a https://doi.org/10.1289/ehp.12053552 DOI |
| 040 | a (SwePub)su | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Arnot, Jon A.4 aut |
| 245 | 1 0 | a Prioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessment |
| 264 | 1 | b Environmental Health Perspectives,c 2012 |
| 338 | a print2 rdacarrier | |
| 500 | a AuthorCount:5; | |
| 520 | a BACKGROUND: Scientists and regulatory agencies strive to identify chemicals that may cause harmful effects to humans and the environment; however, prioritization is challenging because of the large number of chemicals requiring evaluation and limited data and resources. OBJECTIVES: We aimed to prioritize chemicals for exposure and exposure potential and obtain a quantitative perspective on research needs to better address uncertainty in screening assessments. METHODS: We used a multimedia mass balance model to prioritize > 12,000 organic chemicals using four far-field human exposure metrics. The propagation of variance (uncertainty) in key chemical information used as model input for calculating exposure metrics was quantified. RESULTS: Modeled human concentrations and intake rates span approximately 17 and 15 orders of magnitude, respectively. Estimates of exposure potential using human concentrations and a unit emission rate span approximately 13 orders of magnitude, and intake fractions span 7 orders of magnitude. The actual chemical emission rate contributes the greatest variance (uncertainty) in exposure estimates. The human biotransformation half-life is the second greatest source of uncertainty in estimated concentrations. In general, biotransformation and biodegradation half-lives are greater sources of uncertainty in modeled exposure and exposure potential than chemical partition coefficients. CONCLUSIONS: Mechanistic exposure modeling is suitable for screening and prioritizing large numbers of chemicals. By including uncertainty analysis and uncertainty in chemical information in the exposure estimates, these methods can help identify and address the important sources of uncertainty in human exposure and risk assessment in a systematic manner. | |
| 650 | 7 | a NATURVETENSKAPx Geovetenskap och miljövetenskapx Miljövetenskap0 (SwePub)105022 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Environmental Sciences0 (SwePub)105022 hsv//eng |
| 650 | 7 | a NATURVETENSKAPx Biologix Ekologi0 (SwePub)106112 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Biological Sciencesx Ecology0 (SwePub)106112 hsv//eng |
| 653 | a exposure | |
| 653 | a high throughput | |
| 653 | a organic chemicals | |
| 653 | a risk | |
| 653 | a uncertainty analysis | |
| 700 | 1 | a Brown, Trevor N.4 aut |
| 700 | 1 | a Wania, Frank4 aut |
| 700 | 1 | a Breivik, Knut4 aut |
| 700 | 1 | a McLachlan, Michael S.u Stockholms universitet,Institutionen för tillämpad miljövetenskap (ITM)4 aut0 (Swepub:su)mmcla |
| 710 | 2 | a Stockholms universitetb Institutionen för tillämpad miljövetenskap (ITM)4 org |
| 773 | 0 | t Journal of Environmental Health Perspectivesd : Environmental Health Perspectivesg 120:11, s. 1565-1570q 120:11<1565-1570x 0091-6765x 1552-9924 |
| 856 | 4 | u https://doi.org/10.1289/ehp.1205355 |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-84774 |
| 856 | 4 8 | u https://doi.org/10.1289/ehp.1205355 |
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